Large language models reduce public knowledge sharing on online Q&A platforms.

Large language models (LLMs) are a potential substitute for human-generated data and knowledge resources. This substitution, however, can present a significant problem for the training data needed to develop future models if it leads to a reduction of human-generated content. In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM.

Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing.

Autonomous Ultrasound Image Quality Assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultrasound images contain many spurious artifacts, such as noise due to handheld probe positioning, errors in the selection of probe parameters and patient respiration during the procedure.

JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition.

Zero-shot learning (ZSL) is a paradigm in transfer learning that aims to recognize unknown categories by having a mere description of them. The problem of ZSL has been thoroughly studied in the domain of static object recognition, however, ZSL for dynamic events (ZSER) such as activities and gestures has hardly been investigated. In this context, this paper addresses ZSER by relying on semantic attributes of events to transfer the learned knowledge from seen classes to unseen ones.

Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence.

Introduction: Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field.

Adaptive Human-Robotic Interaction for Robotic-assisted Surgical Settings.

Introduction: Increased complexity in robotic-assisted surgical system interfaces introduces problems with human-robot collaboration that result in excessive mental workload (MWL), adversely impacting a surgeon's task performance and increasing error probability. Real-time monitoring of the operator's MWL will aid in identifying when and how interventions can be best provided to moderate MWL. In this study, an MWL-based adaptive automation system is constructed and evaluated for its effectiveness during robotic-assisted surgery.